{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm_npm-z-aimcp-server","slug":"npm-z-aimcp-server","name":"@z_ai/mcp-server","type":"mcp","url":"https://www.npmjs.com/package/@z_ai/mcp-server","page_url":"https://unfragile.ai/npm-z-aimcp-server","categories":["mcp-servers"],"tags":["zai","mcp","vision"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm_npm-z-aimcp-server__cap_0","uri":"capability://tool.use.integration.mcp.server.protocol.implementation.with.ai.model.integration","name":"mcp server protocol implementation with ai model integration","description":"Implements the Model Context Protocol (MCP) specification as a server that bridges client applications to AI models and tools. Uses MCP's standardized JSON-RPC message format over stdio/SSE transports to expose resources, tools, and prompts to compatible clients. Handles bidirectional communication patterns where clients request capabilities and the server responds with structured tool definitions and execution results.","intents":["I want to expose AI capabilities to Claude, VS Code, or other MCP-compatible clients without building custom integrations","I need to standardize how my AI tools communicate with multiple client applications using a protocol-based approach","I want to create a server that clients can discover and invoke AI-powered functions through a standard interface"],"best_for":["teams building AI agent infrastructure with multiple client integrations","developers creating reusable AI tool libraries that need protocol-agnostic exposure","organizations standardizing on MCP for AI capability distribution"],"limitations":["MCP adoption is still emerging — fewer clients support it compared to REST/OpenAPI","Debugging MCP communication requires understanding JSON-RPC message flow and transport layers","No built-in authentication — relies on client-side security and transport-layer encryption"],"requires":["Node.js 16+ (typical for npm packages)","MCP client implementation or compatible application (Claude, Cline, etc.)","Understanding of JSON-RPC 2.0 protocol specification"],"input_types":["JSON-RPC requests","tool invocation parameters","resource URIs"],"output_types":["JSON-RPC responses","tool execution results","resource content"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-z-aimcp-server__cap_1","uri":"capability://image.visual.image.generation.capability.exposure.via.mcp.tools","name":"image generation capability exposure via mcp tools","description":"Exposes image generation functionality as callable MCP tools that clients can invoke through the protocol. Wraps underlying image generation models (likely DALL-E, Stable Diffusion, or similar) as MCP tool definitions with schema-validated parameters like prompt, size, and style. Handles tool invocation requests, passes them to the image generation backend, and returns image URLs or base64-encoded results to the client.","intents":["I want Claude or another MCP client to generate images on demand without leaving the application","I need to expose image generation as a tool that AI agents can call as part of multi-step workflows","I want to standardize image generation requests across multiple client applications using MCP"],"best_for":["AI agent builders who need image generation as a composable tool","teams integrating image generation into Claude-based workflows","developers building MCP-compatible creative AI applications"],"limitations":["Image generation latency (typically 5-30 seconds) may timeout if client has strict response deadlines","Underlying image model capabilities and quality depend on the backend service used","No built-in image caching — repeated identical requests generate new images each time","Rate limiting depends on the underlying image generation service (DALL-E, Stable Diffusion, etc.)"],"requires":["API key or credentials for the underlying image generation service","MCP client that supports tool invocation (Claude, Cline, or custom implementation)","Network connectivity to the image generation backend"],"input_types":["text prompts","generation parameters (size, style, model variant)"],"output_types":["image URLs","base64-encoded image data","image metadata (dimensions, model used)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-z-aimcp-server__cap_2","uri":"capability://tool.use.integration.tool.schema.definition.and.client.discovery","name":"tool schema definition and client discovery","description":"Defines and advertises available tools to MCP clients through standardized JSON schemas that describe parameters, return types, and tool metadata. When clients connect, the server responds to tool discovery requests with a list of available tools and their schemas, enabling clients to understand what can be invoked and validate parameters before sending requests. Uses JSON Schema format for parameter validation and description.","intents":["I want MCP clients to automatically discover what AI tools are available without hardcoding tool lists","I need to ensure clients send properly formatted parameters by validating against a schema","I want to provide rich documentation about each tool so clients can decide when to invoke it"],"best_for":["developers building extensible MCP servers with dynamic tool registration","teams that need client-side tool discovery and validation","AI agent frameworks that need to understand available capabilities at runtime"],"limitations":["Schema complexity is limited by JSON Schema expressiveness — some complex validation logic must be implemented server-side","Tool discovery is synchronous — large numbers of tools (100+) may cause noticeable latency in client startup","Schema changes require client reconnection to discover new tools"],"requires":["JSON Schema knowledge for defining tool parameters","MCP client that supports tool discovery (most modern MCP clients do)","Consistent tool naming and schema structure across the server"],"input_types":["tool definition objects with name, description, parameters schema"],"output_types":["JSON Schema definitions","tool metadata and descriptions","parameter validation results"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-z-aimcp-server__cap_3","uri":"capability://memory.knowledge.resource.exposure.and.content.serving.via.mcp","name":"resource exposure and content serving via mcp","description":"Exposes static or dynamic resources (documents, data, configurations) as MCP resources that clients can read through the protocol. Resources are identified by URIs and can return text, JSON, or binary content. The server implements the MCP resource protocol to handle read requests, list available resources, and provide resource metadata like MIME type and size.","intents":["I want Claude to access knowledge bases or documentation without making separate API calls","I need to expose configuration or context data to MCP clients in a standardized way","I want clients to discover and read resources without knowing their exact paths"],"best_for":["teams building knowledge-augmented AI agents that need access to internal documentation","developers creating MCP servers that serve both tools and data resources","organizations standardizing on MCP for AI context distribution"],"limitations":["Large resources (>10MB) may cause memory issues if loaded entirely into memory","No built-in caching — repeated resource reads hit the backend each time","Resource listing can be slow if there are thousands of resources","No built-in versioning — clients always get the current resource state"],"requires":["MCP client that supports resource reading","Resource data source (file system, database, API, etc.)","Proper MIME type configuration for different resource types"],"input_types":["resource URIs","resource list queries"],"output_types":["resource content (text, JSON, binary)","resource metadata (MIME type, size, last modified)","resource listings"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-z-aimcp-server__cap_4","uri":"capability://text.generation.language.prompt.template.exposure.and.client.side.invocation","name":"prompt template exposure and client-side invocation","description":"Defines and exposes reusable prompt templates as MCP prompts that clients can discover and invoke with parameters. Templates can include placeholders for dynamic values, and the server returns the rendered prompt text to the client. Enables clients to use server-defined prompts for consistent AI interactions without embedding prompt logic client-side.","intents":["I want to centralize prompt engineering on the server so all clients use consistent prompts","I need Claude to invoke predefined prompts with parameters rather than constructing prompts dynamically","I want to version and update prompts server-side without changing client code"],"best_for":["teams with centralized prompt engineering practices","organizations that need consistency across multiple AI agent clients","developers building prompt-as-a-service architectures"],"limitations":["Prompt templates are limited to text substitution — complex conditional logic must be implemented server-side","Clients must know prompt names in advance or discover them through the MCP protocol","No built-in prompt versioning — clients always get the current prompt version"],"requires":["MCP client that supports prompt invocation","Prompt template definitions with parameter placeholders","Consistent naming convention for prompts across the server"],"input_types":["prompt names","prompt parameters (key-value pairs)"],"output_types":["rendered prompt text","prompt metadata (description, parameters)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-z-aimcp-server__cap_5","uri":"capability://tool.use.integration.stdio.and.sse.transport.abstraction.for.mcp.communication","name":"stdio and sse transport abstraction for mcp communication","description":"Abstracts the underlying transport layer for MCP communication, supporting both stdio (for local/CLI clients) and Server-Sent Events (SSE) for HTTP-based clients. Handles message serialization/deserialization, connection lifecycle, and error handling across different transport mechanisms. Allows the same MCP server implementation to work with multiple client types without transport-specific code.","intents":["I want my MCP server to work with both CLI tools and web-based clients without duplicating code","I need to support stdio for local development and SSE for production HTTP clients","I want to abstract transport complexity so I can focus on tool/resource implementation"],"best_for":["developers building MCP servers that need to support multiple client types","teams deploying MCP servers in both local and cloud environments","organizations standardizing on MCP with diverse client ecosystems"],"limitations":["Stdio transport is limited to local/subprocess communication — not suitable for remote clients","SSE is unidirectional (server-to-client) for streaming — bidirectional communication requires a separate channel","Transport abstraction adds complexity — debugging transport issues requires understanding both stdio and SSE","Connection pooling and multiplexing are not built-in — each client gets a separate connection"],"requires":["Node.js with stdio support (all versions)","HTTP server for SSE transport (Express, Fastify, etc.)","MCP client that supports the desired transport (stdio or SSE)"],"input_types":["JSON-RPC messages over stdio","JSON-RPC messages over HTTP SSE"],"output_types":["JSON-RPC responses over stdio","JSON-RPC responses over HTTP SSE"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (typical for npm packages)","MCP client implementation or compatible application (Claude, Cline, etc.)","Understanding of JSON-RPC 2.0 protocol specification","API key or credentials for the underlying image generation service","MCP client that supports tool invocation (Claude, Cline, or custom implementation)","Network connectivity to the image generation backend","JSON Schema knowledge for defining tool parameters","MCP client that supports tool discovery (most modern MCP clients do)","Consistent tool naming and schema structure across the server","MCP client that supports resource reading"],"failure_modes":["MCP adoption is still emerging — fewer clients support it compared to REST/OpenAPI","Debugging MCP communication requires understanding JSON-RPC message flow and transport layers","No built-in authentication — relies on client-side security and transport-layer encryption","Image generation latency (typically 5-30 seconds) may timeout if client has strict response deadlines","Underlying image model capabilities and quality depend on the backend service used","No built-in image caching — repeated identical requests generate new images each time","Rate limiting depends on the underlying image generation service (DALL-E, Stable Diffusion, etc.)","Schema complexity is limited by JSON Schema expressiveness — some complex validation logic must be implemented server-side","Tool discovery is synchronous — large numbers of tools (100+) may cause noticeable latency in client startup","Schema changes require client reconnection to discover new tools","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4905106126457851,"quality":0.22,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T14:23:33.350Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":14175,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-z-aimcp-server","compare_url":"https://unfragile.ai/compare?artifact=npm-z-aimcp-server"}},"signature":"zd/CjVao18YDhi45yUKCRGw+LK7T6CHWr23h2CDYiglPZl88F0EUkEROP/giDJMdU7hmIhHxkTyEUYkSJl5tAg==","signedAt":"2026-06-19T20:30:08.227Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-z-aimcp-server","artifact":"https://unfragile.ai/npm-z-aimcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=npm-z-aimcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}